Big Data analytics for privacy through ND-homomorphic encryption. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Saravanan, S.; Poornima, N.
• Is Part Of: Journal of control and decision. Volume 10:Issue 1(2023)
• Page Start: 64
• Page End: 71
• Publication Date: 2023-01-02
• Subjects: Security and confidentiality -- vast statistics data -- encryption -- nondeterministic -- fully homomorphic encryption; Control theory -- Periodicals; Decision making -- Periodicals; Robotics -- Periodicals; 003.505
• DOI: 10.1080/23307706.2022.2067252
• Languages: English
• ISSNs: 2330-7706

Abstract

Abstract : Rapidly rising the quantity of Big Data is an opportunity to flout the privacy of people. When high processing capacity and massive storage are required for Big Data, distributed networks have been used. There are several people involved in these activities, the system may contribute to privacy infringements frameworks have been developed for the preservation of privacy at various levels (e.g. information age, information the executives and information preparing) as for the existing pattern of huge information. We plan to frame this paper as a literature survey of these classifications, including the Privacy Processes in Big Data and the presentation of the Associate Challenges. Homomorphic encryption is particularised aimed at solitary single action on the ciphered information. Homomorphic enciphering is restrained to an honest operation on the encoded data. The reference to encryption project fulfils many accurate trading operations on coded numerical data; therefore, it protects the written in code-sensible information even more.